The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models

By Chandra R. Bhat. 2014

50 citations recorded by the source corpus.

This monograph presents the basics of the composite marginal likelihood (CML) inference approach, discussing the asymptotic properties of the CML estimator and the advantages and limitations of the approach. The composite marginal likelihood (CML) inference approach is a relatively simple approach that can be used when the full likelihood function is practically infeasible to evaluate due to underlying complex dependencies. The history of the approach may be traced back to the pseudo-likelihood approach of Besag (1974) for modeling spatial data, and has found traction in a variety of fields since, including genetics, spatial statistics, longitudinal analyses, and multivariate modeling. However, the CML method has found little coverage in the econometrics field, especially in discrete choice modeling. This monograph fills this gap by identifying the value and potential applications of the method in discrete dependent variable modeling as well as mixed discrete and continuous dependent variable model systems. In particular, it develops a blueprint (complete with matrix notation) to apply the CML estimation technique to a wide variety of discrete and mixed dependent variable models.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/917c32286ac62df9c7ef1fb2ac7f1746137f387a

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Citations in the corpus

A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling (2014, background): “…good empirical efficiency of the MACML estimator, 9 Note that the CML estimator score functions are unbiased and the CML estimator has the asymptotic properties of being consistent and normally distributed under usual regularity conditions (see Bhat, 2011, Xu and Reid, 2011, and Bhat, 2014).”
A New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variables (2015, methodology): “q q MACML MACML L L δ δ (22) The covariance matrix of the parameters δ may be estimated by the inverse of Godambe’s (1960) sandwich information matrix (see Zhao and Joe, 2005; Bhat, 2014).”
Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model (2015, background): “5 A complete description of Composite Marginal Likelihood (CML) approach is beyond the scope of this paper; readers are referred to Bhat (2014) for a comprehensive discussion of the CML approach in the context of choice modeling. analytically approximated log-likelihood surface and leading to…”